Can the cloud help the smallest, most vulnerable patients—those in the neonatal intensive care unit? Based upon the Artemis Cloud platform, a commercial service, researchers at McMaster Children’s Hospital built an analytical model to determine what would be needed to capture, analyze in real time, and store patient data. The Artemis cloud platform can collect up to 13 physiological measures (blood pressure, electrocardiogram, heart rate, etc.) plus infusion pump data from each patient, de-identify them, and transmit them to the cloud. There, medical algorithms—such as one that flags patients at risk for late onset neonatal sepsis and one that detects sleep apnea—are applied to streams of patient data. The data are stored for later retrospective analysis and data mining to uncover correlations and patterns of clinical relevance.

Borrowing from queuing theory, the mathematical study of wait times originally used for calls routed through telephone exchanges, the researchers modeled the performance of Artemis Cloud. Patient features—gestational age, preterm/term, complex/standard—served as inputs to the model. Assumptions included: that service is rendered in order of patient arrival and that the unit operates at full capacity, 47 beds. The model predicted a mean of 47.8 patients in the entire intensive care unit, with a stay of 41.5 days per patient and 9 medical algorithms per patient per day. The researchers calculated that nearly 10 gigabytes of memory space would be needed to store both pump and patient data just for one day. A cluster of 40 central processing units could provide the required computational processing power for McMaster, even if the NICU grew from 47 to 170 beds.

This study determines the infrastructure needed to support both computationally and memory-intensive activities. The idea of providing clinical support and early detection of disease through analytics is tantalizing, but only through practical implementation can we begin to reap the benefits of healthcare analytics.